CleanSlop vs Solarch: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CleanSlop and Solarch — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
CleanSlop
CleanSlop
Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.
Key features
- Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
- System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
- Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
- Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
- Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
- Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
- GitHub App Integration: Scan only the repositories you select, read-only access.
- Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.
Best for
- A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
- A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
- A side-project builder verifies that one user's data cannot be read by another through database policies.
- A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
- A team lead gets a short prioritized list instead of hundreds of linter warnings.
- An indie hacker books an expert triage call to confirm an ambiguous finding.
Solarch
Solarch
Visual backend architecture tool: draw node/edge graphs, validate with a rules engine, and generate matching backend code to prevent architectural drift.
Key features
- Live Graph Editor: A node-and-edge visual editor for composing backend architectures (services, data stores, connections) with live feedback and manipulation.
- Rules Engine Validation: Built-in validation rules that check the drawn architecture for policy, best-practice, and safety violations before code generation.
- Automated Code Generation: Generates scaffolded backend code that directly matches the validated architecture, reducing manual translation work.
- Architecture-Code Parity: Keeps diagrams and generated code in sync to prevent architectural drift and ensure the implementation follows the approved design.
- Scaffold Customization: Produces customizable starter code and project structure so teams can iterate from a working baseline rather than from scratch.
- Design-as-Source-of-Truth: Treats architecture diagrams as the authoritative specification, enabling governance, review, and reproducible builds from the visual model.
- Live node/edge graph editor for backend architecture
- Rules engine to validate architecture diagrams against constraints
- Automated code generation that produces backend code matching the diagram
- Prevents architectural drift by keeping design and code in sync
- Visual-to-code workflow enabling design-driven development
Best for
- Designing Microservice Backends: Visually model microservice boundaries, communication paths, and data stores, then generate matching service scaffolds to accelerate implementation.
- Onboarding and Handoff: Provide new engineers with a validated visual architecture plus generated starter code so they can quickly understand and contribute to the system.
- Enforcing Architecture Governance: Apply organizational rules in the validation engine to ensure new designs comply with standards before code is produced.
- Prototype-to-Production Acceleration: Rapidly iterate on architecture diagrams and produce working code prototypes that can be extended into production systems.
- Refactoring and Reconciliation: Use the visual model to plan refactors and produce updated scaffold code that brings implementation back into alignment with the intended architecture.
- Documentation-as-Code: Maintain diagrams as the source of truth and regenerate code or artifacts to keep documentation and implementation synchronized.
- Designing backend system architecture as diagrams and generating initial code scaffolding
- Enforcing architectural constraints across teams via automated validation
- Keeping infrastructure-as-code and implementation consistent with architecture diagrams
- Generating API or service stubs from a validated architecture graph
- Onboarding and documentation through visual architecture artifacts tied to code